Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm

Long, Jun; Wu, Shimin; Han, Xiaodong; Wang, Yunbo; Liu, Limin

doi:10.3390/aerospace10010070

Open AccessArticle

Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm

by

Jun Long

^1,2,

Shimin Wu

^2,3,4

,

Xiaodong Han

⁵,

Yunbo Wang

^1,2,*

and

Limin Liu

^2,3,4,*

¹

Big Data Institute, Central South University, Changsha 410075, China

²

Joint Laboratory of Space Information System, Changsha 410075, China

³

School of Computer Science and Engineering, Central South University, Changsha 410075, China

⁴

Network Resource Management and Trust Evaluation Key Laboratory of Hunan, Changsha 410075, China

⁵

Communications and Navigation Satellite General Department, China Academy of Space Technology, Beijing 100098, China

^*

Authors to whom correspondence should be addressed.

Aerospace 2023, 10(1), 70; https://doi.org/10.3390/aerospace10010070

Submission received: 29 November 2022 / Revised: 1 January 2023 / Accepted: 4 January 2023 / Published: 10 January 2023

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration)

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Abstract

The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the complex and changeable environment in space also poses challenges to the management of multi-satellite systems (MSS). To address the above issues, this paper formulates a mixed integer optimization problem to solve the autonomous task planning for MSS. First, we constructed a multi-agent-based on-board autonomous management and multi-satellite collaboration architecture. Based on this architecture, we propose a hybrid genetic algorithm with simulated annealing (H-GASA) to solve the multi-satellite cooperative autonomous task planning (MSCATP). With the H-GASA, a heuristic task scheduling scheme was developed to deal with possible task conflicts in MSCATP. Finally, a simulation scenario was established to validate our proposed H-GASA, which exhibits a superior performance in terms of computational power and success rate compared to existing algorithms.

Keywords: multi-satellite system; autonomous task planning; multi-agent; genetic algorithm; simulated annealing

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MDPI and ACS Style

Long, J.; Wu, S.; Han, X.; Wang, Y.; Liu, L. Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm. Aerospace 2023, 10, 70. https://doi.org/10.3390/aerospace10010070

AMA Style

Long J, Wu S, Han X, Wang Y, Liu L. Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm. Aerospace. 2023; 10(1):70. https://doi.org/10.3390/aerospace10010070

Chicago/Turabian Style

Long, Jun, Shimin Wu, Xiaodong Han, Yunbo Wang, and Limin Liu. 2023. "Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm" Aerospace 10, no. 1: 70. https://doi.org/10.3390/aerospace10010070

APA Style

Long, J., Wu, S., Han, X., Wang, Y., & Liu, L. (2023). Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm. Aerospace, 10(1), 70. https://doi.org/10.3390/aerospace10010070

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm

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